------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\Dr. Badas\Dropbox\1 - Research\98 - Coauthored Projects\Gender & Clerkships\Proj
> ect Two - Feeder Judges, Ideology, Gender\2 - Data and Analysis\Replication for JLC\BadasSandersStau
> fferJLCreplication.log
  log type:  text
 opened on:  24 Jul 2024, 09:57:08

. 
. use "BadasSandersStaufferJCLdata.dta"

. 
. ///: Figure 1 
> twoway (histogram cfscore if FemaleClerk==1 , percent width(.15) color(blue%25)) ///
>        (histogram cfscore if FemaleClerk==0 & cfscore > -.4, percent width(.15) ///        
>            color(red%25)) ///
>            (histogram judgecfimputed if FemaleJudge==1, percent width(.15) color(black%25)) ///
>            (histogram judgecfimputed if FemaleJudge==0, percent width(.15) color(green%25)), ///
>            legend (order(1 "Female Clerks" 2 "Male Clerks" 3 "Female Judges" 4 "Male Judges") /// 
>            on ring(0))

.            
.            
. ///: Table 1
> logit FemaleClerk i.FemaleJudge  i.CourtType same  judgecfimputed  i.year 

Iteration 0:   log likelihood =  -9993.065  
Iteration 1:   log likelihood = -9875.7412  
Iteration 2:   log likelihood = -9875.7013  
Iteration 3:   log likelihood = -9875.7013  

Logistic regression                                     Number of obs = 14,429
                                                        LR chi2(15)   = 234.73
                                                        Prob > chi2   = 0.0000
Log likelihood = -9875.7013                             Pseudo R2     = 0.0117

--------------------------------------------------------------------------------
   FemaleClerk | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
 1.FemaleJudge |      0.128      0.043    2.986   0.003        0.044       0.212
               |
     CourtType |
         USDC  |      0.458      0.037   12.385   0.000        0.385       0.530
         USSC  |     -0.282      0.137   -2.058   0.040       -0.551      -0.013
               |
 samelawschool |      0.104      0.040    2.585   0.010        0.025       0.182
judgecfimputed |     -0.081      0.024   -3.358   0.001       -0.128      -0.034
               |
          year |
         1996  |      0.049      0.081    0.608   0.543       -0.109       0.207
         1997  |      0.079      0.081    0.986   0.324       -0.078       0.237
         1998  |      0.116      0.080    1.449   0.147       -0.041       0.272
         1999  |      0.060      0.079    0.756   0.450       -0.095       0.214
         2000  |      0.122      0.078    1.570   0.116       -0.030       0.275
         2001  |      0.128      0.078    1.651   0.099       -0.024       0.280
         2002  |      0.167      0.077    2.159   0.031        0.015       0.318
         2003  |      0.153      0.079    1.946   0.052       -0.001       0.307
         2004  |      0.148      0.079    1.874   0.061       -0.007       0.303
         2005  |      0.310      0.360    0.861   0.389       -0.395       1.015
               |
         _cons |     -0.526      0.065   -8.099   0.000       -0.653      -0.398
--------------------------------------------------------------------------------

. 
. 
. ///: Figure 2
> margins FemaleJudge, plot

Predictive margins                                      Number of obs = 14,429
Model VCE: OIM

Expression: Pr(FemaleClerk), predict()

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 FemaleJudge |
          0  |      0.476      0.005  101.387   0.000        0.467       0.485
          1  |      0.508      0.009   54.738   0.000        0.490       0.526
------------------------------------------------------------------------------

Variables that uniquely identify margins: FemaleJudge

. 
. ///: Figure 3
> margins, at(judgecfimputed=(-1.5(.5)1.67)) 

Predictive margins                                      Number of obs = 14,429
Model VCE: OIM

Expression: Pr(FemaleClerk), predict()
1._at: judgecfimputed = -1.5
2._at: judgecfimputed =   -1
3._at: judgecfimputed =  -.5
4._at: judgecfimputed =    0
5._at: judgecfimputed =   .5
6._at: judgecfimputed =    1
7._at: judgecfimputed =  1.5

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |      0.513      0.010   51.489   0.000        0.494       0.533
          2  |      0.503      0.007   68.233   0.000        0.489       0.518
          3  |      0.494      0.005   95.037   0.000        0.483       0.504
          4  |      0.484      0.004  116.875   0.000        0.475       0.492
          5  |      0.474      0.005   94.989   0.000        0.464       0.483
          6  |      0.464      0.007   65.585   0.000        0.450       0.478
          7  |      0.454      0.010   47.279   0.000        0.435       0.473
------------------------------------------------------------------------------

. marginsplot, title(Ideology and Hiring Women Clerks) plot1opts(color(black)msymbol(none) lwidth(medt
> hick) ///
> xtitle(Judge Conservativism (CF Score)) ytitle("Predicted Probability" "Hiring Women Clerk")) ///
> recastci(rarea)  ///
> ciopts(color(gs10%85)alwidth(none)) ///
> addplot(hist judgecfimputed, fcolor(gs1%30) lwidth(none) ///
> percent ///
> yaxis(2) ///
> yscale(alt lcolor(gs10) axis(2)) ///
> ylabel(0 "0%" 5 "5%" 10 "10%" 30 " "  , /// 
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// 
> ytitle(" ", axis(2)) /// 
> xlabel(-1.5(1)1.67) ///
> legend(off))

Variables that uniquely identify margins: judgecfimputed

. 
. ///: Table  2
> logit FemaleClerk i.FemaleJudge##c.judgecfimputed i.CourtType same  i.year 

Iteration 0:   log likelihood =  -9993.065  
Iteration 1:   log likelihood =  -9875.741  
Iteration 2:   log likelihood = -9875.7011  
Iteration 3:   log likelihood = -9875.7011  

Logistic regression                                     Number of obs = 14,429
                                                        LR chi2(16)   = 234.73
                                                        Prob > chi2   = 0.0000
Log likelihood = -9875.7011                             Pseudo R2     = 0.0117

----------------------------------------------------------------------------------------------
                 FemaleClerk | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------------------+----------------------------------------------------------------
               1.FemaleJudge |      0.128      0.045    2.819   0.005        0.039       0.217
              judgecfimputed |     -0.081      0.027   -2.973   0.003       -0.135      -0.028
                             |
FemaleJudge#c.judgecfimputed |
                          1  |      0.001      0.058    0.019   0.985       -0.113       0.115
                             |
                   CourtType |
                       USDC  |      0.458      0.037   12.378   0.000        0.385       0.530
                       USSC  |     -0.282      0.137   -2.054   0.040       -0.551      -0.013
                             |
               samelawschool |      0.104      0.040    2.585   0.010        0.025       0.182
                             |
                        year |
                       1996  |      0.049      0.081    0.608   0.543       -0.109       0.207
                       1997  |      0.079      0.081    0.986   0.324       -0.078       0.237
                       1998  |      0.116      0.080    1.449   0.147       -0.041       0.272
                       1999  |      0.060      0.079    0.755   0.450       -0.095       0.214
                       2000  |      0.122      0.078    1.569   0.117       -0.030       0.275
                       2001  |      0.128      0.078    1.651   0.099       -0.024       0.280
                       2002  |      0.167      0.077    2.158   0.031        0.015       0.318
                       2003  |      0.153      0.079    1.945   0.052       -0.001       0.307
                       2004  |      0.148      0.079    1.873   0.061       -0.007       0.303
                       2005  |      0.310      0.360    0.861   0.389       -0.395       1.015
                             |
                       _cons |     -0.526      0.065   -8.085   0.000       -0.653      -0.398
----------------------------------------------------------------------------------------------

. 
. ///: Figure 4, Right Panel
> margins, dydx(FemaleJudge) at(judgecfimputed=(-1.5(.15)1.67)) 

Average marginal effects                                Number of obs = 14,429
Model VCE: OIM

Expression: Pr(FemaleClerk), predict()
dy/dx wrt:  1.FemaleJudge
1._at:  judgecfimputed =  -1.5
2._at:  judgecfimputed = -1.35
3._at:  judgecfimputed =  -1.2
4._at:  judgecfimputed = -1.05
5._at:  judgecfimputed =   -.9
6._at:  judgecfimputed =  -.75
7._at:  judgecfimputed =   -.6
8._at:  judgecfimputed =  -.45
9._at:  judgecfimputed =   -.3
10._at: judgecfimputed =  -.15
11._at: judgecfimputed =     0
12._at: judgecfimputed =   .15
13._at: judgecfimputed =    .3
14._at: judgecfimputed =   .45
15._at: judgecfimputed =    .6
16._at: judgecfimputed =   .75
17._at: judgecfimputed =    .9
18._at: judgecfimputed =  1.05
19._at: judgecfimputed =   1.2
20._at: judgecfimputed =  1.35
21._at: judgecfimputed =   1.5
22._at: judgecfimputed =  1.65

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
0.FemaleJudge  |  (base outcome)
---------------+----------------------------------------------------------------
1.FemaleJudge  |
           _at |
            1  |      0.031      0.021    1.513   0.130       -0.009       0.071
            2  |      0.031      0.019    1.661   0.097       -0.006       0.068
            3  |      0.031      0.017    1.832   0.067       -0.002       0.065
            4  |      0.031      0.015    2.028   0.043        0.001       0.062
            5  |      0.031      0.014    2.248   0.025        0.004       0.059
            6  |      0.031      0.013    2.482   0.013        0.007       0.056
            7  |      0.031      0.012    2.709   0.007        0.009       0.054
            8  |      0.031      0.011    2.891   0.004        0.010       0.053
            9  |      0.032      0.011    2.983   0.003        0.011       0.052
           10  |      0.032      0.011    2.956   0.003        0.011       0.053
           11  |      0.032      0.011    2.820   0.005        0.010       0.054
           12  |      0.032      0.012    2.615   0.009        0.008       0.055
           13  |      0.032      0.013    2.384   0.017        0.006       0.058
           14  |      0.032      0.015    2.158   0.031        0.003       0.060
           15  |      0.032      0.016    1.952   0.051       -0.000       0.064
           16  |      0.032      0.018    1.770   0.077       -0.003       0.067
           17  |      0.032      0.020    1.612   0.107       -0.007       0.070
           18  |      0.032      0.022    1.476   0.140       -0.010       0.074
           19  |      0.032      0.023    1.358   0.174       -0.014       0.078
           20  |      0.032      0.025    1.256   0.209       -0.018       0.082
           21  |      0.032      0.027    1.167   0.243       -0.022       0.085
           22  |      0.032      0.029    1.089   0.276       -0.026       0.089
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, title(Ideology and Hiring Women Clerks) plot1opts(color(black)msymbol(none) lwidth(medt
> hick) ///
> xtitle(Judge Conservativism (CF Score)) ytitle("Predicted Probability" "Hiring Women Clerk")) ///
> recastci(rarea)  ///
> ciopts(color(gs10%85)alwidth(none)) ///
> addplot(hist judgecfimputed, fcolor(gs1%30) lwidth(none) ///
> percent ///
> yaxis(2) ///
> yscale(alt lcolor(gs10) axis(2)) ///
> ylabel(0 "0%" 5 "5%" 10 "10%" 30 " "  , /// 
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// 
> ytitle(" ", axis(2)) /// 
> xlabel(-1.5(1)1.67) ///
> ylabel(-.05(.025).1) ///
> yline(0) ///
> legend(off))

Variables that uniquely identify margins: judgecfimputed

. 
. ///: Figure 4, Left Panel 
> margins, by(FemaleJudge) at(judgecfimputed=(-1.5(.15)1.67)) 

Predictive margins                                      Number of obs = 14,429
Model VCE: OIM

Expression: Pr(FemaleClerk), predict()
Over:       FemaleJudge
1._at: 0.FemaleJudge
            judgecfimputed =  -1.5
       1.FemaleJudge
            judgecfimputed =  -1.5
2._at: 0.FemaleJudge
            judgecfimputed = -1.35
       1.FemaleJudge
            judgecfimputed = -1.35
3._at: 0.FemaleJudge
            judgecfimputed =  -1.2
       1.FemaleJudge
            judgecfimputed =  -1.2
4._at: 0.FemaleJudge
            judgecfimputed = -1.05
       1.FemaleJudge
            judgecfimputed = -1.05
5._at: 0.FemaleJudge
            judgecfimputed =   -.9
       1.FemaleJudge
            judgecfimputed =   -.9
6._at: 0.FemaleJudge
            judgecfimputed =  -.75
       1.FemaleJudge
            judgecfimputed =  -.75
7._at: 0.FemaleJudge
            judgecfimputed =   -.6
       1.FemaleJudge
            judgecfimputed =   -.6
8._at: 0.FemaleJudge
            judgecfimputed =  -.45
       1.FemaleJudge
            judgecfimputed =  -.45
9._at: 0.FemaleJudge
            judgecfimputed =   -.3
       1.FemaleJudge
            judgecfimputed =   -.3
10._at: 0.FemaleJudge
            judgecfimputed =  -.15
        1.FemaleJudge
            judgecfimputed =  -.15
11._at: 0.FemaleJudge
            judgecfimputed =     0
        1.FemaleJudge
            judgecfimputed =     0
12._at: 0.FemaleJudge
            judgecfimputed =   .15
        1.FemaleJudge
            judgecfimputed =   .15
13._at: 0.FemaleJudge
            judgecfimputed =    .3
        1.FemaleJudge
            judgecfimputed =    .3
14._at: 0.FemaleJudge
            judgecfimputed =   .45
        1.FemaleJudge
            judgecfimputed =   .45
15._at: 0.FemaleJudge
            judgecfimputed =    .6
        1.FemaleJudge
            judgecfimputed =    .6
16._at: 0.FemaleJudge
            judgecfimputed =   .75
        1.FemaleJudge
            judgecfimputed =   .75
17._at: 0.FemaleJudge
            judgecfimputed =    .9
        1.FemaleJudge
            judgecfimputed =    .9
18._at: 0.FemaleJudge
            judgecfimputed =  1.05
        1.FemaleJudge
            judgecfimputed =  1.05
19._at: 0.FemaleJudge
            judgecfimputed =   1.2
        1.FemaleJudge
            judgecfimputed =   1.2
20._at: 0.FemaleJudge
            judgecfimputed =  1.35
        1.FemaleJudge
            judgecfimputed =  1.35
21._at: 0.FemaleJudge
            judgecfimputed =   1.5
        1.FemaleJudge
            judgecfimputed =   1.5
22._at: 0.FemaleJudge
            judgecfimputed =  1.65
        1.FemaleJudge
            judgecfimputed =  1.65

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
_at#FemaleJudge |
           1 0  |      0.507      0.012   42.376   0.000        0.484       0.531
           1 1  |      0.537      0.017   32.037   0.000        0.504       0.570
           2 0  |      0.504      0.011   45.639   0.000        0.483       0.526
           2 1  |      0.534      0.015   35.114   0.000        0.504       0.564
           3 0  |      0.501      0.010   49.419   0.000        0.481       0.521
           3 1  |      0.531      0.014   38.669   0.000        0.504       0.558
           4 0  |      0.498      0.009   53.821   0.000        0.480       0.516
           4 1  |      0.528      0.012   42.703   0.000        0.504       0.552
           5 0  |      0.495      0.008   58.959   0.000        0.479       0.512
           5 1  |      0.525      0.011   47.095   0.000        0.503       0.547
           6 0  |      0.492      0.008   64.943   0.000        0.477       0.507
           6 1  |      0.522      0.010   51.494   0.000        0.502       0.542
           7 0  |      0.489      0.007   71.834   0.000        0.476       0.503
           7 1  |      0.519      0.009   55.207   0.000        0.501       0.538
           8 0  |      0.486      0.006   79.547   0.000        0.474       0.498
           8 1  |      0.516      0.009   57.280   0.000        0.499       0.534
           9 0  |      0.483      0.006   87.664   0.000        0.472       0.494
           9 1  |      0.513      0.009   56.967   0.000        0.496       0.531
          10 0  |      0.480      0.005   95.193   0.000        0.470       0.490
          10 1  |      0.510      0.009   54.299   0.000        0.492       0.529
          11 0  |      0.477      0.005  100.488   0.000        0.468       0.486
          11 1  |      0.507      0.010   50.070   0.000        0.487       0.527
          12 0  |      0.474      0.005  101.819   0.000        0.465       0.483
          12 1  |      0.504      0.011   45.254   0.000        0.482       0.526
          13 0  |      0.471      0.005   98.560   0.000        0.462       0.481
          13 1  |      0.501      0.012   40.541   0.000        0.477       0.526
          14 0  |      0.468      0.005   91.747   0.000        0.458       0.478
          14 1  |      0.498      0.014   36.265   0.000        0.471       0.525
          15 0  |      0.465      0.006   83.235   0.000        0.454       0.476
          15 1  |      0.495      0.015   32.528   0.000        0.466       0.525
          16 0  |      0.462      0.006   74.560   0.000        0.450       0.474
          16 1  |      0.492      0.017   29.313   0.000        0.460       0.525
          17 0  |      0.459      0.007   66.556   0.000        0.446       0.473
          17 1  |      0.490      0.018   26.561   0.000        0.453       0.526
          18 0  |      0.456      0.008   59.521   0.000        0.441       0.471
          18 1  |      0.487      0.020   24.203   0.000        0.447       0.526
          19 0  |      0.453      0.008   53.471   0.000        0.437       0.470
          19 1  |      0.484      0.022   22.173   0.000        0.441       0.526
          20 0  |      0.450      0.009   48.306   0.000        0.432       0.469
          20 1  |      0.481      0.024   20.416   0.000        0.435       0.527
          21 0  |      0.447      0.010   43.896   0.000        0.427       0.467
          21 1  |      0.478      0.025   18.885   0.000        0.428       0.527
          22 0  |      0.444      0.011   40.116   0.000        0.423       0.466
          22 1  |      0.475      0.027   17.544   0.000        0.422       0.528
---------------------------------------------------------------------------------

. marginsplot, title(Ideology and Hiring Women Clerks) plot1opts(color(black)msymbol(none) lwidth(medt
> hick) ///
> xtitle(Judge Conservativism (CF Score)) ytitle("Predicted Probability" "Hiring Women Clerk")) ///
> recastci(rarea)  ///
> ciopts(color(gs10%85)alwidth(none)) ///
> addplot(hist judgecfimputed, fcolor(gs1%30) lwidth(none) ///
> percent ///
> yaxis(2) ///
> yscale(alt lcolor(gs10) axis(2)) ///
> ylabel(0 "0%" 5 "5%" 10 "10%" 30 " "  , /// 
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// 
> ytitle(" ", axis(2)) /// 
> xlabel(-1.5(1)1.67) ///
> yline(0) ///
> legend(off))

Variables that uniquely identify margins: judgecfimputed FemaleJudge

. 
. 
. 
. ///: Figure 4
> histogram IdeologicalDistance
(bin=38, start=.0005185, width=.14934405)

. 
. 
. 
. ///: Table 3
> reg IdeologicalDistance (i.FemaleJudge c.judgecfimputed)##i.FemaleClerk cfscore previous Total samel
> aw  i.year i.CourtType 

      Source |       SS           df       MS      Number of obs   =     5,880
-------------+----------------------------------   F(21, 5858)     =    189.10
       Model |  1191.40973        21  56.7337964   Prob > F        =    0.0000
    Residual |  1757.55377     5,858  .300026249   R-squared       =    0.4040
-------------+----------------------------------   Adj R-squared   =    0.4019
       Total |  2948.96349     5,879  .501609712   Root MSE        =    .54775

----------------------------------------------------------------------------------------------
         IdeologicalDistance | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------------------+----------------------------------------------------------------
               1.FemaleJudge |     -0.049      0.023   -2.088   0.037       -0.094      -0.003
              judgecfimputed |      0.463      0.013   34.997   0.000        0.437       0.489
               1.FemaleClerk |      0.016      0.017    0.923   0.356       -0.018       0.050
                             |
     FemaleJudge#FemaleClerk |
                        1 1  |     -0.036      0.037   -0.984   0.325       -0.108       0.036
                             |
FemaleClerk#c.judgecfimputed |
                          1  |      0.217      0.021   10.184   0.000        0.175       0.259
                             |
                     cfscore |     -0.338      0.008  -41.500   0.000       -0.354      -0.322
                    previous |      0.049      0.078    0.635   0.525       -0.103       0.201
             TotalClerkYears |     -0.027      0.008   -3.405   0.001       -0.042      -0.011
               samelawschool |     -0.021      0.017   -1.229   0.219       -0.053       0.012
                             |
                        year |
                       1996  |      0.039      0.033    1.195   0.232       -0.025       0.103
                       1997  |      0.028      0.032    0.871   0.384       -0.035       0.092
                       1998  |      0.031      0.032    0.971   0.332       -0.032       0.094
                       1999  |      0.024      0.032    0.729   0.466       -0.040       0.087
                       2000  |      0.046      0.032    1.424   0.154       -0.017       0.109
                       2001  |      0.018      0.032    0.565   0.572       -0.045       0.081
                       2002  |      0.052      0.032    1.623   0.105       -0.011       0.115
                       2003  |      0.079      0.034    2.336   0.020        0.013       0.146
                       2004  |      0.082      0.034    2.409   0.016        0.015       0.148
                       2005  |     -0.095      0.135   -0.705   0.481       -0.359       0.169
                             |
                   CourtType |
                       USDC  |      0.007      0.015    0.480   0.632       -0.023       0.037
                       USSC  |      0.006      0.059    0.108   0.914       -0.110       0.122
                             |
                       _cons |      0.736      0.026   27.991   0.000        0.685       0.788
----------------------------------------------------------------------------------------------

. 
. 
. ///: Figure 6
> ///: Figure 6 requires downloading the mplotoffset package
> ssc install mplotoffset 
checking mplotoffset consistency and verifying not already installed...
all files already exist and are up to date.

. 
. margins FemaleJudge, by(FemaleClerk) 

Predictive margins                                       Number of obs = 5,880
Model VCE: OLS

Expression: Linear prediction, predict()
Over:       FemaleClerk

-----------------------------------------------------------------------------------------
                        |            Delta-method
                        |     Margin   std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
FemaleClerk#FemaleJudge |
                   0 0  |      0.957      0.010   93.580   0.000        0.937       0.977
                   0 1  |      0.909      0.021   44.276   0.000        0.868       0.949
                   1 0  |      1.021      0.014   74.508   0.000        0.995       1.048
                   1 1  |      0.937      0.024   38.806   0.000        0.890       0.984
-----------------------------------------------------------------------------------------

. mplotoffset, offset(.02) legend(ring(0))

  Variables that uniquely identify margins: FemaleJudge FemaleClerk

. 
. 
. ///: Figure 7
> margins, dydx(FemaleClerk) at(judgecfimputed=(-1.5(.5)1.5)) plot(recastci(rarea))

Average marginal effects                                 Number of obs = 5,880
Model VCE: OLS

Expression: Linear prediction, predict()
dy/dx wrt:  1.FemaleClerk
1._at: judgecfimputed = -1.5
2._at: judgecfimputed =   -1
3._at: judgecfimputed =  -.5
4._at: judgecfimputed =    0
5._at: judgecfimputed =   .5
6._at: judgecfimputed =    1
7._at: judgecfimputed =  1.5

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
0.FemaleClerk  |  (base outcome)
---------------+----------------------------------------------------------------
1.FemaleClerk  |
           _at |
            1  |     -0.317      0.035   -9.111   0.000       -0.385      -0.249
            2  |     -0.209      0.026   -8.133   0.000       -0.259      -0.158
            3  |     -0.100      0.018   -5.504   0.000       -0.136      -0.065
            4  |      0.008      0.015    0.527   0.598       -0.022       0.038
            5  |      0.116      0.019    6.142   0.000        0.079       0.154
            6  |      0.225      0.027    8.422   0.000        0.173       0.277
            7  |      0.333      0.036    9.269   0.000        0.263       0.404
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Variables that uniquely identify margins: judgecfimputed

. margins FemaleClerk, at(judgecfimputed=(-1.5(.5)1.5)) plot(recastci(rarea) legend(ring(0)))

Predictive margins                                       Number of obs = 5,880
Model VCE: OLS

Expression: Linear prediction, predict()
1._at: judgecfimputed = -1.5
2._at: judgecfimputed =   -1
3._at: judgecfimputed =  -.5
4._at: judgecfimputed =    0
5._at: judgecfimputed =   .5
6._at: judgecfimputed =    1
7._at: judgecfimputed =  1.5

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
_at#FemaleClerk |
           1 0  |      0.270      0.022   12.181   0.000        0.227       0.314
           1 1  |     -0.047      0.028   -1.697   0.090       -0.101       0.007
           2 0  |      0.502      0.016   30.620   0.000        0.470       0.534
           2 1  |      0.293      0.020   14.499   0.000        0.254       0.333
           3 0  |      0.734      0.012   63.748   0.000        0.711       0.756
           3 1  |      0.633      0.014   44.456   0.000        0.605       0.661
           4 0  |      0.965      0.009  105.515   0.000        0.947       0.983
           4 1  |      0.973      0.012   81.330   0.000        0.950       0.997
           5 0  |      1.197      0.011  108.126   0.000        1.175       1.218
           5 1  |      1.313      0.015   86.615   0.000        1.283       1.343
           6 0  |      1.428      0.016   90.532   0.000        1.397       1.459
           6 1  |      1.653      0.022   76.839   0.000        1.611       1.695
           7 0  |      1.660      0.022   77.150   0.000        1.618       1.702
           7 1  |      1.993      0.029   68.678   0.000        1.936       2.050
---------------------------------------------------------------------------------

Variables that uniquely identify margins: judgecfimputed FemaleClerk

. 
. 
. clear

. 
. log close
      name:  <unnamed>
       log:  C:\Users\Dr. Badas\Dropbox\1 - Research\98 - Coauthored Projects\Gender & Clerkships\Proj
> ect Two - Feeder Judges, Ideology, Gender\2 - Data and Analysis\Replication for JLC\BadasSandersStau
> fferJLCreplication.log
  log type:  text
 closed on:  24 Jul 2024, 09:57:36
------------------------------------------------------------------------------------------------------
